A deep neural network as surrogate model for forward simulation of borehole resistivity measurements

M. Shahriari, D. Pardo, B. Moser, F. Sobieczky. A deep neural network as surrogate model for forward simulation of borehole resistivity measurements. Procedia Manufacturing, volume 42, pages 235-238, DOI https://doi.org/10.1016/j.promfg.2020.02.075, 3, 2020.

Autoren
  • Mostafa Shahriari
  • David Pardo
  • Bernhard A. Moser
  • Florian Sobieczky
TypArtikel
JournalProcedia Manufacturing
Band42
DOIhttps://doi.org/10.1016/j.promfg.2020.02.075
Monat3
Jahr2020
Seiten235-238
Abstract

Inverse problems appear in multiple industrial applications. Solving such inverse problems require the repeated solution of the forward problem. This is the most time-consuming stage when employing inversion techniques, and it constitutes a severe limitation when the inversion needs to be performed in real-time. In here, we focus on the real-time inversion of resistivity measurements for geosteering. We investigate the use of a deep neural network (DNN) to approximate the forward function arising from Maxwell’s equations, which govern the electromagnetic wave propagation through a media. By doing so, the evaluation of the forward problems is performed offline, allowing for the online real-time evaluation
(inversion) of the DNN.